Podcasts about cloud firestore

Arnaud et Emmanuel discutent des nouvelles de ce mois. On y parle intégrité de JVM, fetch size de JDBC, MCP, de prompt engineering, de DeepSeek bien sûr mais aussi de Maven 4 et des proxy de répository Maven. Et d'autres choses encore, bonne lecture. Enregistré le 7 février 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-322.mp3 ou en vidéo sur YouTube. News Langages Les evolutions de la JVM pour augmenter l'intégrité https://inside.java/2025/01/03/evolving-default-integrity/ un article sur les raisons pour lesquelles les editeurs de frameworks et les utilisateurs s'arrachent les cheveux et vont continuer garantir l'integrite du code et des données en enlevant des APIs existantes historiquemnt agents dynamiques, setAccessible, Unsafe, JNI Article expliques les risques percus par les mainteneurs de la JVM Franchement c'est un peu leg sur les causes l'article, auto propagande JavaScript Temporal, enfin une API propre et moderne pour gérer les dates en JS https://developer.mozilla.org/en-US/blog/javascript-temporal-is-coming/ JavaScript Temporal est un nouvel objet conçu pour remplacer l'objet Date, qui présente des défauts. Il résout des problèmes tels que le manque de prise en charge des fuseaux horaires et la mutabilité. Temporal introduit des concepts tels que les instants, les heures civiles et les durées. Il fournit des classes pour gérer diverses représentations de date/heure, y compris celles qui tiennent compte du fuseau horaire et celles qui n'en tiennent pas compte. Temporal simplifie l'utilisation de différents calendriers (par exemple, chinois, hébreu). Il comprend des méthodes pour les comparaisons, les conversions et le formatage des dates et des heures. La prise en charge par les navigateurs est expérimentale, Firefox Nightly ayant l'implémentation la plus aboutie. Un polyfill est disponible pour essayer Temporal dans n'importe quel navigateur. Librairies Un article sur les fetch size du JDBC et les impacts sur vos applications https://in.relation.to/2025/01/24/jdbc-fetch-size/ qui connait la valeur fetch size par default de son driver? en fonction de vos use cases, ca peut etre devastateur exemple d'une appli qui retourne 12 lignes et un fetch size de oracle a 10, 2 a/r pour rien et si c'est 50 lignres retournées la base de donnée est le facteur limitant, pas Java donc monter sont fetch size est avantageux, on utilise la memoire de Java pour eviter la latence Quarkus annouce les MCP servers project pour collecter les servier MCP en Java https://quarkus.io/blog/introducing-mcp-servers/ MCP d'Anthropic introspecteur de bases JDBC lecteur de filke system Dessine en Java FX demarrables facilement avec jbang et testes avec claude desktop, goose et mcp-cli permet d'utliser le pouvoir des librarires Java de votre IA d'ailleurs Spring a la version 0.6 de leur support MCP https://spring.io/blog/2025/01/23/spring-ai-mcp-0 Infrastructure Apache Flink sur Kibernetes https://www.decodable.co/blog/get-running-with-apache-flink-on-kubernetes-2 un article tres complet ejn deux parties sur l'installation de Flink sur Kubernetes installation, setup mais aussi le checkpointing, la HA, l'observablité Data et Intelligence Artificielle 10 techniques de prompt engineering https://medium.com/google-cloud/10-prompt-engineering-techniques-every-beginner-should-know-bf6c195916c7 Si vous voulez aller plus loin, l'article référence un très bon livre blanc sur le prompt engineering https://www.kaggle.com/whitepaper-prompt-engineering Les techniques évoquées : Zero-Shot Prompting: On demande directement à l'IA de répondre à une question sans lui fournir d'exemple préalable. C'est comme si on posait une question à une personne sans lui donner de contexte. Few-Shot Prompting: On donne à l'IA un ou plusieurs exemples de la tâche qu'on souhaite qu'elle accomplisse. C'est comme montrer à quelqu'un comment faire quelque chose avant de lui demander de le faire. System Prompting: On définit le contexte général et le but de la tâche pour l'IA. C'est comme donner à l'IA des instructions générales sur ce qu'elle doit faire. Role Prompting: On attribue un rôle spécifique à l'IA (enseignant, journaliste, etc.). C'est comme demander à quelqu'un de jouer un rôle spécifique. Contextual Prompting: On fournit des informations supplémentaires ou un contexte pour la tâche. C'est comme donner à quelqu'un toutes les informations nécessaires pour répondre à une question. Step-Back Prompting: On pose d'abord une question générale, puis on utilise la réponse pour poser une question plus spécifique. C'est comme poser une question ouverte avant de poser une question plus fermée. Chain-of-Thought Prompting: On demande à l'IA de montrer étape par étape comment elle arrive à sa conclusion. C'est comme demander à quelqu'un d'expliquer son raisonnement. Self-Consistency Prompting: On pose plusieurs fois la même question à l'IA et on compare les réponses pour trouver la plus cohérente. C'est comme vérifier une réponse en la posant sous différentes formes. Tree-of-Thoughts Prompting: On permet à l'IA d'explorer plusieurs chemins de raisonnement en même temps. C'est comme considérer toutes les options possibles avant de prendre une décision. ReAct Prompting: On permet à l'IA d'interagir avec des outils externes pour résoudre des problèmes complexes. C'est comme donner à quelqu'un les outils nécessaires pour résoudre un problème. Les patterns GenAI the thoughtworks https://martinfowler.com/articles/gen-ai-patterns/ tres introductif et pre RAG le direct prompt qui est un appel direct au LLM: limitations de connaissance et de controle de l'experience eval: evaluer la sortie d'un LLM avec plusieurs techniques mais fondamentalement une fonction qui prend la demande, la reponse et donc un score numerique evaluation via un LLM (le meme ou un autre), ou evaluation humaine tourner les evaluations a partir de la chaine de build amis aussi en live vu que les LLMs puvent evoluer. Decrit les embedding notament d'image amis aussi de texte avec la notion de contexte DeepSeek et la fin de la domination de NVidia https://youtubetranscriptoptimizer.com/blog/05_the_short_case_for_nvda un article sur les raisons pour lesquelles NVIDIA va se faire cahllengert sur ses marges 90% de marge quand meme parce que les plus gros GPU et CUDA qui est proprio mais des approches ardware alternatives existent qui sont plus efficientes (TPU et gros waffle) Google, MS et d'autres construisent leurs GPU alternatifs CUDA devient de moins en moins le linga franca avec l'investissement sur des langages intermediares alternatifs par Apple, Google OpenAI etc L'article parle de DeepSkeek qui est venu mettre une baffe dans le monde des LLMs Ils ont construit un competiteur a gpt4o et o1 avec 5M de dollars et des capacites de raisonnements impressionnant la cles c'etait beaucoup de trick d'optimisation mais le plus gros est d'avoir des poids de neurores sur 8 bits vs 32 pour les autres. et donc de quatizer au fil de l'eau et au moment de l'entrainement beaucoup de reinforcemnt learning innovatifs aussi et des Mixture of Expert donc ~50x moins chers que OpenAI Donc plus besoin de GPU qui on des tonnes de vRAM ah et DeepSeek est open source un article de semianalytics change un peu le narratif le papier de DeepSkeek en dit long via ses omissions par ensemple les 6M c'est juste l'inference en GPU, pas les couts de recherches et divers trials et erreurs en comparaison Claude Sonnet a coute 10M en infererence DeepSeek a beaucoup de CPU pre ban et ceratins post bans evalués a 5 Milliards en investissement. leurs avancées et leur ouverture reste extremement interessante Une intro à Apache Iceberg http://blog.ippon.fr/2025/01/17/la-revolution-des-donnees-lavenement-des-lakehouses-avec-apache-iceberg/ issue des limites du data lake. non structuré et des Data Warehouses aux limites en diversite de données et de volume entrent les lakehouse Et particulierement Apache Iceberg issue de Netflix gestion de schema mais flexible notion de copy en write vs merge on read en fonction de besoins garantie atomicite, coherence, isoliation et durabilite notion de time travel et rollback partitions cachées (qui abstraient la partition et ses transfos) et evolution de partitions compatbile avec les moteurs de calcul comme spark, trino, flink etc explique la structure des metadonnées et des données Guillaume s'amuse à générer des histoires courtes de Science-Fiction en programmant des Agents IA avec LangChain4j et aussi avec des workflows https://glaforge.dev/posts/2025/01/27/an-ai-agent-to-generate-short-scifi-stories/ https://glaforge.dev/posts/2025/01/31/a-genai-agent-with-a-real-workflow/ Création d'un générateur automatisé de nouvelles de science-fiction à l'aide de Gemini et Imagen en Java, LangChain4j, sur Google Cloud. Le système génère chaque nuit des histoires, complétées par des illustrations créées par le modèle Imagen 3, et les publie sur un site Web. Une étape d'auto-réflexion utilise Gemini pour sélectionner la meilleure image pour chaque chapitre. L'agent utilise un workflow explicite, drivé par le code Java, où les étapes sont prédéfinies dans le code, plutôt que de s'appuyer sur une planification basée sur LLM. Le code est disponible sur GitHub et l'application est déployée sur Google Cloud. L'article oppose les agents de workflow explicites aux agents autonomes, en soulignant les compromis de chaque approche. Car parfois, les Agent IA autonomes qui gèrent leur propre planning hallucinent un peu trop et n'établissent pas un plan correctement, ou ne le suive pas comme il faut, voire hallucine des “function call”. Le projet utilise Cloud Build, le Cloud Run jobs, Cloud Scheduler, Firestore comme base de données, et Firebase pour le déploiement et l'automatisation du frontend. Dans le deuxième article, L'approche est différente, Guillaume utilise un outil de Workflow, plutôt que de diriger le planning avec du code Java. L'approche impérative utilise du code Java explicite pour orchestrer le workflow, offrant ainsi un contrôle et une parallélisation précis. L'approche déclarative utilise un fichier YAML pour définir le workflow, en spécifiant les étapes, les entrées, les sorties et l'ordre d'exécution. Le workflow comprend les étapes permettant de générer une histoire avec Gemini 2, de créer une invite d'image, de générer des images avec Imagen 3 et d'enregistrer le résultat dans Cloud Firestore (base de donnée NoSQL). Les principaux avantages de l'approche impérative sont un contrôle précis, une parallélisation explicite et des outils de programmation familiers. Les principaux avantages de l'approche déclarative sont des définitions de workflow peut-être plus faciles à comprendre (même si c'est un YAML, berk !) la visualisation, l'évolutivité et une maintenance simplifiée (on peut juste changer le YAML dans la console, comme au bon vieux temps du PHP en prod). Les inconvénients de l'approche impérative incluent le besoin de connaissances en programmation, les défis potentiels en matière de maintenance et la gestion des conteneurs. Les inconvénients de l'approche déclarative incluent une création YAML pénible, un contrôle de parallélisation limité, l'absence d'émulateur local et un débogage moins intuitif. Le choix entre les approches dépend des exigences du projet, la déclarative étant adaptée aux workflows plus simples. L'article conclut que la planification déclarative peut aider les agents IA à rester concentrés et prévisibles. Outillage Vulnérabilité des proxy Maven https://github.blog/security/vulnerability-research/attacks-on-maven-proxy-repositories/ Quelque soit le langage, la techno, il est hautement conseillé de mettre en place des gestionnaires de repositories en tant que proxy pour mieux contrôler les dépendances qui contribuent à la création de vos produits Michael Stepankin de l'équipe GitHub Security Lab a cherché a savoir si ces derniers ne sont pas aussi sources de vulnérabilité en étudiant quelques CVEs sur des produits comme JFrog Artifactory, Sonatype Nexus, et Reposilite Certaines failles viennent de la UI des produits qui permettent d'afficher les artifacts (ex: mettez un JS dans un fichier POM) et même de naviguer dedans (ex: voir le contenu d'un jar / zip et on exploite l'API pour lire, voir modifier des fichiers du serveur en dehors des archives) Les artifacts peuvent aussi être compromis en jouant sur les paramètres propriétaires des URLs ou en jouant sur le nomage avec les encodings. Bref, rien n'est simple ni niveau. Tout système rajoute de la compléxité et il est important de les tenir à mettre à jour. Il faut surveiller activement sa chaine de distribution via différents moyens et ne pas tout miser sur le repository manager. L'auteur a fait une présentation sur le sujet : https://www.youtube.com/watch?v=0Z_QXtk0Z54 Apache Maven 4… Bientôt, c'est promis …. qu'est ce qu'il y aura dedans ? https://gnodet.github.io/maven4-presentation/ Et aussi https://github.com/Bukama/MavenStuff/blob/main/Maven4/whatsnewinmaven4.md Apache Maven 4 Doucement mais surement …. c'est le principe d'un projet Maven 4.0.0-rc-2 est dispo (Dec 2024). Maven a plus de 20 ans et est largement utilisé dans l'écosystème Java. La compatibilité ascendante a toujours été une priorité, mais elle a limité la flexibilité. Maven 4 introduit des changements significatifs, notamment un nouveau schéma de construction et des améliorations du code. Changements du POM Séparation du Build-POM et du Consumer-POM : Build-POM : Contient des informations propres à la construction (ex. plugins, configurations). Consumer-POM : Contient uniquement les informations nécessaires aux consommateurs d'artefacts (ex. dépendances). Nouveau Modèle Version 4.1.0 : Utilisé uniquement pour le Build-POM, alors que le Consumer-POM reste en 4.0.0 pour la compatibilité. Introduit de nouveaux éléments et en marque certains comme obsolètes. Modules renommés en sous-projets : “Modules” devient “Sous-projets” pour éviter la confusion avec les Modules Java. L'élément remplace (qui reste pris en charge). Nouveau type de packaging : “bom” (Bill of Materials) : Différencie les POMs parents et les BOMs de gestion des dépendances. Prend en charge les exclusions et les imports basés sur les classifiers. Déclaration explicite du répertoire racine : permet de définir explicitement le répertoire racine du projet. Élimine toute ambiguïté sur la localisation des racines de projet. Nouvelles variables de répertoire : ${project.rootDirectory}, ${session.topDirectory} et ${session.rootDirectory} pour une meilleure gestion des chemins. Remplace les anciennes solutions non officielles et variables internes obsolètes. Prise en charge de syntaxes alternatives pour le POM Introduction de ModelParser SPI permettant des syntaxes alternatives pour le POM. Apache Maven Hocon Extension est un exemple précoce de cette fonctionnalité. Améliorations pour les sous-projets Versioning automatique des parents Il n'est plus nécessaire de définir la version des parents dans chaque sous-projet. Fonctionne avec le modèle de version 4.1.0 et s'étend aux dépendances internes au projet. Support complet des variables compatibles CI Le Flatten Maven Plugin n'est plus requis. Prend en charge les variables comme ${revision} pour le versioning. Peut être défini via maven.config ou la ligne de commande (mvn verify -Drevision=4.0.1). Améliorations et corrections du Reactor Correction de bug : Gestion améliorée de --also-make lors de la reprise des builds. Nouvelle option --resume (-r) pour redémarrer à partir du dernier sous-projet en échec. Les sous-projets déjà construits avec succès sont ignorés lors de la reprise. Constructions sensibles aux sous-dossiers : Possibilité d'exécuter des outils sur des sous-projets sélectionnés uniquement. Recommandation : Utiliser mvn verify plutôt que mvn clean install. Autres Améliorations Timestamps cohérents pour tous les sous-projets dans les archives packagées. Déploiement amélioré : Le déploiement ne se produit que si tous les sous-projets sont construits avec succès. Changements de workflow, cycle de vie et exécution Java 17 requis pour exécuter Maven Java 17 est le JDK minimum requis pour exécuter Maven 4. Les anciennes versions de Java peuvent toujours être ciblées pour la compilation via Maven Toolchains. Java 17 a été préféré à Java 21 en raison d'un support à long terme plus étendu. Mise à jour des plugins et maintenance des applications Suppression des fonctionnalités obsolètes (ex. Plexus Containers, expressions ${pom.}). Mise à jour du Super POM, modifiant les versions par défaut des plugins. Les builds peuvent se comporter différemment ; définissez des versions fixes des plugins pour éviter les changements inattendus. Maven 4 affiche un avertissement si des versions par défaut sont utilisées. Nouveau paramètre “Fail on Severity” Le build peut échouer si des messages de log atteignent un niveau de gravité spécifique (ex. WARN). Utilisable via --fail-on-severity WARN ou -fos WARN. Maven Shell (mvnsh) Chaque exécution de mvn nécessitait auparavant un redémarrage complet de Java/Maven. Maven 4 introduit Maven Shell (mvnsh), qui maintient un processus Maven résident unique ouvert pour plusieurs commandes. Améliore la performance et réduit les temps de build. Alternative : Utilisez Maven Daemon (mvnd), qui gère un pool de processus Maven résidents. Architecture Un article sur les feature flags avec Unleash https://feeds.feedblitz.com//911939960/0/baeldungImplement-Feature-Flags-in-Java-With-Unleash Pour A/B testing et des cycles de développements plus rapides pour « tester en prod » Montre comment tourner sous docker unleash Et ajouter la librairie a du code java pour tester un feature flag Sécurité Keycloak 26.1 https://www.keycloak.org/2025/01/keycloak-2610-released.html detection des noeuds via la proble base de donnée aulieu echange reseau virtual threads pour infinispan et jgroups opentelemetry tracing supporté et plein de fonctionalités de sécurité Loi, société et organisation Les grands morceaux du coût et revenus d'une conférence. Ici http://bdx.io|bdx.io https://bsky.app/profile/ameliebenoit33.bsky.social/post/3lgzslhedzk2a 44% le billet 52% les sponsors 38% loc du lieu 29% traiteur et café 12% standiste 5% frais speaker (donc pas tous) Ask Me Anything Julien de Provin: J'aime beaucoup le mode “continuous testing” de Quarkus, et je me demandais s'il existait une alternative en dehors de Quarkus, ou à défaut, des ressources sur son fonctionnement ? J'aimerais beaucoup avoir un outil agnostique utilisable sur les projets non-Quarkus sur lesquels j'intervient, quitte à y metttre un peu d'huile de coude (ou de phalange pour le coup). https://github.com/infinitest/infinitest/ Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 février 2025 : Touraine Tech - Tours (France) 21 février 2025 : LyonJS 100 - Lyon (France) 28 février 2025 : Paris TS La Conf - Paris (France) 6 mars 2025 : DevCon #24 : 100% IA - Paris (France) 13 mars 2025 : Oracle CloudWorld Tour Paris - Paris (France) 14 mars 2025 : Rust In Paris 2025 - Paris (France) 19-21 mars 2025 : React Paris - Paris (France) 20 mars 2025 : PGDay Paris - Paris (France) 20-21 mars 2025 : Agile Niort - Niort (France) 25 mars 2025 : ParisTestConf - Paris (France) 26-29 mars 2025 : JChateau Unconference 2025 - Cour-Cheverny (France) 27-28 mars 2025 : SymfonyLive Paris 2025 - Paris (France) 28 mars 2025 : DataDays - Lille (France) 28-29 mars 2025 : Agile Games France 2025 - Lille (France) 3 avril 2025 : DotJS - Paris (France) 3 avril 2025 : SoCraTes Rennes 2025 - Rennes (France) 4 avril 2025 : Flutter Connection 2025 - Paris (France) 4 avril 2025 : aMP Orléans 04-04-2025 - Orléans (France) 10-11 avril 2025 : Android Makers - Montrouge (France) 10-12 avril 2025 : Devoxx Greece - Athens (Greece) 16-18 avril 2025 : Devoxx France - Paris (France) 23-25 avril 2025 : MODERN ENDPOINT MANAGEMENT EMEA SUMMIT 2025 - Paris (France) 24 avril 2025 : IA Data Day 2025 - Strasbourg (France) 29-30 avril 2025 : MixIT - Lyon (France) 7-9 mai 2025 : Devoxx UK - London (UK) 15 mai 2025 : Cloud Toulouse - Toulouse (France) 16 mai 2025 : AFUP Day 2025 Lille - Lille (France) 16 mai 2025 : AFUP Day 2025 Lyon - Lyon (France) 16 mai 2025 : AFUP Day 2025 Poitiers - Poitiers (France) 24 mai 2025 : Polycloud - Montpellier (France) 24 mai 2025 : NG Baguette Conf 2025 - Nantes (France) 5-6 juin 2025 : AlpesCraft - Grenoble (France) 5-6 juin 2025 : Devquest 2025 - Niort (France) 10-11 juin 2025 : Modern Workplace Conference Paris 2025 - Paris (France) 11-13 juin 2025 : Devoxx Poland - Krakow (Poland) 12-13 juin 2025 : Agile Tour Toulouse - Toulouse (France) 12-13 juin 2025 : DevLille - Lille (France) 13 juin 2025 : Tech F'Est 2025 - Nancy (France) 17 juin 2025 : Mobilis In Mobile - Nantes (France) 24 juin 2025 : WAX 2025 - Aix-en-Provence (France) 25-26 juin 2025 : Agi'Lille 2025 - Lille (France) 25-27 juin 2025 : BreizhCamp 2025 - Rennes (France) 26-27 juin 2025 : Sunny Tech - Montpellier (France) 1-4 juillet 2025 : Open edX Conference - 2025 - Palaiseau (France) 7-9 juillet 2025 : Riviera DEV 2025 - Sophia Antipolis (France) 18-19 septembre 2025 : API Platform Conference - Lille (France) & Online 2-3 octobre 2025 : Volcamp - Clermont-Ferrand (France) 6-10 octobre 2025 : Devoxx Belgium - Antwerp (Belgium) 9-10 octobre 2025 : Forum PHP 2025 - Marne-la-Vallée (France) 16-17 octobre 2025 : DevFest Nantes - Nantes (France) 4-7 novembre 2025 : NewCrafts 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12-14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 28-31 janvier 2026 : SnowCamp 2026 - Grenoble (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 17 juin 2026 : Devoxx Poland - Krakow (Poland) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Firebase Security Rules are a powerful feature that allows you to control access to your app's data in Firebase Realtime Database, Cloud Firestore, and Cloud Storage. --- Send in a voice message: https://podcasters.spotify.com/pod/show/codingcatdev/message Support this podcast: https://podcasters.spotify.com/pod/show/codingcatdev/support

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We're finishing out 2021 with a celebration of our favorite episodes and topics from the year! From new tools for Cost Optimization in GKE and advances in AI to tips for improving feelings of imposter syndrome, Carter Morgan, Stephanie Wong, and Mark Mirchandani share memorable moments from 2021 and look forward to future episodes. Carter Morgan Carter Morgan is Developer Advocate for Google Cloud, where he creates and hosts content on Google's Youtube channel, co-hosts several Google Cloud podcasts, and designs courses like the Udacity course “Scalable Microservices with Kubernetes” he co-created with Kelsey Hightower. Carter Morgan is an international standup comedian, who's approach of creating unique moments with the audience in front of him has seen him perform all over the world, including in Paris, London, the Melbourne International Comedy Festival with Joe White. And in 2019, and the 2019 Edinburgh Fringe Festival. Previously, he was a programmer for the USAF and Microsoft. Stephanie Wong Stephanie Wong is a Developer Advocate focusing on online content across all Google Cloud products. She's a host of the GCP Podcast and the Where the Internet Lives podcast, along with many GCP Youtube video series. She is the winner of a 2021 Webby Award for her content about data centers. Previously she was a Customer Engineer at Google and at Oracle. Outside of her tech life she is a former pageant queen and hip hop dancer and has an unhealthy obsession with dogs. Mark Mirchandani Mark Mirchandani is a developer advocate for Google Cloud, occasional host of the Google Cloud Platform podcast, and helps create content for users. Cool things of the week Anthos Multi-Cloud v2 is generally available docs Machine learning, Google Kubernetes Engine, and more: 10 free training offers to take advantage of before 2022 blog The past, present, and future of Kubernetes with Eric Brewer blog GCP Podcast Episode 124: VP of Infrastructure Eric Brewer podcast Our Favorite Episodes of 2021 Mark's Favorites GCP Podcast Episode 252: GKE Cost Optimization with Kaslin Fields and Anthony Bushong podcast GCP Podcast Episode 267: Cloud Firestore for Users who are new to Firestore podcast GKE Essentials videos Beyond Your Bill vidoes Stephanie's Favorites GCP Podcast Episode 270: Traditional vs. Service Networking with Ryan Przybyl podcast GCP Podcast Episode 271: The Future of Service Networking with Ryan Przybyl podcast GCP Podcast Episode 279: MLB with Perry Pierce and JoAnn Brereton podcast Carter's Favorites GCP Podcast Episode 284: State of DevOps Report 2021 with Nathen Harvey and Dustin Smith podcast GCP Podcast Episode 287: Imposter Syndrome with Carter Morgan podcast Most Popular Episodes of 2021 GCP Podcast Episode Episode 264: SRE III with Steve McGhee and Yuri Grinshtey podcast GCP Podcast Episode 258: The Power of Serverless with Aparna Sinha and Philip Beevers podcast GCP Podcast Episode 253: Data Governance with Jessi Ashdown and Uri Gilad podcast GCP Podcast Episode 263: SAP + Apigee: The Power of APIs with Benjamin Schuler and Dave Feuer podcast GCP Podcast Episode 271: The Future of Service Networking with Ryan Przybyl podcast Sound Effects Attribution “Dun Dun Duuun” by Divenorth of Freesound.org “Cash Register” by Kiddpark of Freesound.org “Jingles and Pings” by BristolStories of HDInteractive.com “Time – Inception Theme” Composed by Hanz Zimmer (super-low-budget midi version) Hosts Stephanie Wong, Carter Morgan and Mark Mirchandani

Brian Dorsey and Mark Mirchandani are talking intro to Firestore this week with fellow Googler Allison Kornher. Allison, a Cloud Technical Resident, starts the show telling us about the program and how it brought her to Firestore. Allison takes us through the differences between SQL and NoSQL databases and describes the four categories of NoSQL databases: family, document, key value, and graph. Firestore is a scalable, flexible NoSQL document database. To illustrate the uses and benefits of Firestore, Allison walks us through a delicious pizza example. Each document in the database belongs to a collection, which is used to organize these documents. Firestore documents are assigned an identifier and can be quickly changed and called within their collections. Because these documents are stored in an implicit schema in key value pairs, developers have control over the details of database organization and data change and growth are easy to manage. The availability of subcollections further adds to the flexibility of Firestore database design. Choosing a database type will depend on the situation, and Allison suggests this starts with a look at CAP theorem. If a document database is your database of choice, Allison gives our listeners tips for getting started with Firestore and clearing any hurdles along the way. Allison Kornher Allison is a Cloud Technical Resident and has worked helping startups looking to join GCP and in the Premium Tier Cloud Support organization with a focus on Storage. Cool things of the week BigQuery admin reference guide: Tables & routines blog Top 25 Google Search terms, now in BigQuery blog Three security and scalability improvements for Cloud SQL for SQL Server blog GCP Podcast Episode 247: Cloud SQL Insights with Nimesh Bhagat podcast GCP Podcast Episode 163: Cloud SQL with Amy Krishnamohan podcast Interview Cloud Firestore site Cloud Firestore Documentation docs Cloud Firestore explained: for users who never used Firestore before blog Gabi on Twitter site Datastore site BigTable site Firebase Realtime Database site Memorystore site Cloud Spanner site GCP Podcast Episode 248: Cloud Spanner Revisited with Dilraj Kaur and Christoph Bussler podcast All you need to know about Firestore: A cheatsheet blog What’s something cool you’re working on? Brian has been working on sharing a persistent disk between Google Compute Engine VMs. Cloud Storage site Cloud Filestore site Cloud SQL site

Google Cloud, AWS y Azure son los proveedores de servicios en la nube dominantes en el mercado actual. El mercado está altamente competitivo y existe un solapo significativo entre los servicios ofrecidos por estos tres proveedores principales. En función del dominio, los profesionales de los datos buscan normalmente una plataforma que se ajuste a sus necesidades y a sus casos de uso. En el episodio de SaaS Product Chat de esta semana hablamos específicamente de las capacidades de Google Cloud Platform y de la variedad de productos y servicios que ofrece en la nube.No olvides comentar aquí en YouTube y sugerir temas o invitados que desearíamos tener en el show.Estos son los enlaces a los temas de los que hemos hablado:Google Cloud: https://cloud.google.com/Andi Gutmans es el director general y VP de ingeniería para bases de datos en Google: https://softwareengineeringdaily.com/2021/03/16/google-cloud-databases-with-andi-gutmans/Estructurando Datos en Cloud Firestore: https://medium.com/canariasjs/estructurando-datos-en-cloud-firestore-3dce2cb1ceaeModelo de datos de Cloud Firestore: https://firebase.google.com/docs/firestore/data-model?hl=esLessons learned from evaluating Cloud Spanner at Uber scale: https://youtu.be/bNqmEnx6ESEFrancisco Solsona, líder de relación con desarrolladores de Google en Latinoamérica y el mundo de habla hispana, en el podcast de TheVentureCity, "Citizen": https://cuonda.com/citizen/cap-5-francisco-solsona-google-devs-splatam-y-la-situacion-del-ecosistema-emprendedorTimnit Gebru sobre desarrollos e investigaciones en torno al reconocimiento facial: https://soundcloud.com/the-impact-podcast/epsiode-93-facial-recognition-demographic-analysis-more-with-timnit-gebruTuit de Jeff Dean: https://twitter.com/jeffdean/status/1270961033616617473Bigquery, el almacén de datos de Google Cloud (Hablemos en Cloud): https://www.youtube.com/watch?v=kud8YDvBKHEGoogle Cloud Platform Podcast: https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5mZWVkYnVybmVyLmNvbS9HY3BQb2RjYXN0¿Qué es Kubernetes? https://kubernetes.io/es/docs/concepts/overview/what-is-kubernetes/Despliegue de aplicaciones sobre Google Cloud Platform con Kubernetes: https://youtu.be/XORU80znx9QSíguenos en Twitter:Danny Prol: https://twitter.com/DannyProl/Claudio Cossio: https://twitter.com/ccossioEstamos en todas estas plataformas:Apple Podcasts: https://podcasts.apple.com/ca/podcast/saas-product-chat/id1435000409ListenNotes: https://www.listennotes.com/podcasts/saas-product-chat-daniel-prol-y-claudio-CABZRIjGVdP/Spotify: https://open.spotify.com/show/36KIhM0DM7nwRLuZ1fVQy3Google Podcasts: https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS8zN3N0Mzg2dg%3D%3D&hl=esBreaker: https://www.breaker.audio/saas-product-chatWeb: https://saasproductchat.com/

Mark and Brian are together this week, hosting our guests Senanu Aggor and Ilias Katsardis as we discuss High Performance Computing with Google. HPC uses powerful computers to solve problems that would otherwise be too large or take too long for standard machines. Innovation and advances in cloud technology have made this resource more accessible, more scalable, and more affordable. Senanu lists some great use cases for HPC, including vehicle manufacturing and the medical field and describes how these markets benefit from the extra power HPC offers. Ilias talks tech and helps us understand the evolution of the Google HPC offering and the architecture most often used with HPC. He explains the benefits of HPC on the cloud over the old way, emphasizing the flexibility of choosing machines based on your code rather than forcing your code onto small machines. Storage of data is flexible, scalable, and secure as well. Diminishing VM to VM latency has been an important advancement in HPC, and Ilias describes how Google has decreased latency. Google Cloud customers are using the HPC offering for all kinds of large computing jobs, and Senanu details some of these real world instances. From Covid vaccine research to disaster evacuation planning, HPC on the cloud is changing the way we process data. Later, Ilias tells our listeners how to get started with their HPC project. Senanu Aggor Senanu Aggor is the Product Marketing Manager for Google Cloud’s High Performance Computing (HPC) solution. Ilias Katsardis Ilias Katsardis is the HPC Solution Lead for the Customer Engineering team (EMEA) at Google. In this role, Ilias brings over 14 years of experience in the cloud computing and high-performance computing industries to promote Google Cloud’s state-of-the-art infrastructure for complex HPC workloads. Previously, he worked as an applications analyst at Cray Inc., where he was a dedicated analyst to the European Centre for Medium-Range Weather Forecasts (ECMWF), and, prior to that, was an HPC application specialist at ClusterVision. Ilias also founded two startups Airwire Networks in 2006 and Performance Hive in 2017. Cool things of the week What’s happening in BigQuery: Time unit partitioning, Table ACLs and more blog BigQuery explained: Blog series blog BigQuery Spotlight videos Cloud Functions vs. Cloud Run video Interview High Performance Computing site GCP Podcast Episode 237: NVIDIA with Bryan Catanzaro podcasdt GCP Podcast Episode 167: World Pi Day with Emma Haruka Iwao podcast Compute Engine site Compute Engine Machine Types site Cloud Storage site Cloud Firestore site Google Cloud with Intel site Cloud GPUs site Best practices for running tightly coupled HPC applications on Compute Engine site Super Computing Event site Stackchat at home This week, Max Saltonstall is talking cyber analytics with Eric Dull from Deloitte.

A wicked Windows zero day is out in the wild without a patch. Google is open sourcing a cool new AI architecture. Instagram wants you to co-watch your feed with a friend. A popular “challenger bank” comes to the US. And the iPad Pro reviews are in.Sponsors:Metalab.coZapier.com/rideLinks: Microsoft says hackers are attacking Windows users with a new unpatched bug (TechCrunch)Apple promises App Store expansion to 20 new countries starting next month (9to5Mac) Google open-sources framework that reduces AI training costs by up to 80% (VentureBeat)Google opens Stadia Makers program for indie game developers (9to5Google) Google unveils Android Performance Tuner, Android GPU Inspector, and Cloud Firestore for game developers (VentureBeat) Instagram has a new way for people isolated by coronavirus to connect: sharing posts via video chat (CNBC) Exclusive: Disney+ Sees Huge Subscription Spike As Homebound Audiences Clamor For Content (Forbes) Revolut launches its neobank in the US (TechCrunch)Review: Apple iPad Pro (2020) (Wired) APPLE IPAD PRO REVIEW 2020: SMALL SPEC BUMP, BIG CAMERA BUMP (The Verge)Buy my book for $2.99 (Kindle) Buy my book for $2.99 (B&N)How the Internet Happened: From Netscape to the iPhone

Google’s own Billy Jacobson joins hosts Mark Mandel and Mark Mirchandani this week to dive deeper into Cloud Bigtable. Bigtable is Google’s petabyte scale, fully managed, NoSQL database. Billy elaborates on what projects Bigtable works best with, like time-series data user analytics, and why it’s such a great tool. It offers huge scalability with the benefits of a managed system, and it’s flexible and easily customized so users can turn on and off the pieces they need. Later, we learn about other programs that are compatible with Bigtable, such as JanusGraph, Open TSDB, and GeoMesa. Bigtable also supports the API for HBase, an open-source project similar to Bigtable. Because of this, it’s easy for HBase users to move to Bigtable, and the Bigtable community has access to many open source libraries. Billy also talks more about the nine clients available, and when customers might want to use Bigtable instead of, or in conjunction with, other Google services such as Spanner and BigQuery. Billy Jacobson Billy Jacobson is a developer programs engineer focusing on Cloud Bigtable. Cool things of the week Introducing Cloud Run Button: Click-to-deploy your git repos to Google Cloud blog Firebase Unity Solutions: Update game behavior without deploying with Remote Config blog Introducing the BigQuery Terraform module blog Macy’s uses Google Cloud to streamline retail operations blog Interview Cloud Bigtable site GCP Podcast Episode 18: Bigtable with Ian Lewis podcast BigQuery site Bigtable Documentation docs Codelab: Introduction to Cloud Bigtable site Key Visualizer docs Bigtable Replication Documentation docs Bigtable and HBase Documentation docs HBase site JanusGraph site Open TSDB site GeoMesa site Bigtable Client Libraries docs Cloud Spanner site Managing IoT Storage with Google’s Cloud Platform (Google I/O’19) video Cloud Datastore site Cloud Firestore site Mapping the invisible: Street View cars add air pollution sensors site Breathing Easy with Bigtable article Question of the week If I have an organization, how do I break down my billing data by folder? Where can you find us next? Mark Mirch is working around town but will be headed to LA soon. Mark Mandel will be at Pax Dev, Pax West, Kubecon, and the GDC Online Games Technology Summit.

On this episode, our hosts Mark Mirchandani and Gabi Ferrara dive into Google Cloud Platform UX with guest and Google Product Designer Michael Kleinerman. Michael’s path to Product Designer started with “ancient” tech designing with Flash and 3D motion graphics and progressed from there through interaction designer to his place now with Google. His experience has helped him appreciate the many different kinds of designers needed for projects and how they have to work together for a good product. At Google, Michael’s team builds design systems that create a balance between what Google uses and what the products built on Google use. He adopted Material Design, which offers guidelines for patterns and components of design, to Google Cloud. Material Design spans across multiple devices and screen sizes to help simplify design across devices. When Cloud reached the enterprise space, where components can be more complex, Michael’s team worked to adjust Cloud using Material Design so that features like tables would work correctly. Accessibility is also a top priority for Cloud and the design team. To begin the process of designing for accessibility, the team finds the top three or so reasons that a user would come to their product and ensures those are accessible to all. The next step is to create easier usability in the second tier features of the product, and then all features beyond. Using a screen reader, they go through the product to see if it’s usable, and really try to make the experience better. The team also makes sure there are a lot of guidance pages as well. The goal in product design is to make things simple and consistent for everyone. Michael Kleinerman Michael is a Product Designer at Google. He worked on Android and YouTube in the Bay Area before joining Cloud in NYC, where he started by leading the UX for Firestore until it launched in both Firebase and GCP. This work evolved into his current role on the core platform team, responsible for the design direction of the main design system used by producer teams to build and launch products on GCP. Cool things of the week Committed use discounts at a glance blog Networking in depth blog Chatbots with Dialog Flow blog and video Turn it up to eleven: Java 11 runtime comes to App Engine blog App Engine second generation runtimes now get double the memory; plus Go 1.12 and PHP 7.3 now generally available blog Interview Material.io site Material Design site Firebase site Cloud Firestore site Question of the week How do I work with my containers locally and then get them into the cloud? Where can you find us next? Gabi is done traveling. Mark Mirch’ is filming for customers in the Bay area. Everyone else is just laying low for now! Sound Effect Attribution “alert.wav” by danielnieto7 of Freesound.org “cell phone vibraion.wav” by MrAuralization of Freesound.org “laugh crowd 2.wav” by MrAuralizationFunWithSound of Freesound.org

Google Developer Advocate Jen Person talks with Mark Mandel and Mark Mirchandani today about developments in Firebase. Firebase is a suite of products that helps developers build apps. According to Jen, it’s equivalent to the client-side of Google Cloud. Firebase works across platforms, including Android, web, iOS and offers many growth features, setting it apart from other Google products. It helps site and app owners interact with and reach customers with services like notifications, remote configurations to optimize the app, testing, and more. Cloud Firestore has come out of beta, and it is available both through Firebase and Google Cloud Platform, making it easy for developers to move from one to the other if their needs change. Recently, the Firebase team has been working to refine their products based on user feedback. Firebase Authentication has been upgraded with the additions of phone authentication, email link authentication, and multiple email actions. They’ve also added a generic authentication option so developers can use any provider they choose. ML Kit makes machine learning much easier for client apps or on the server. With on-device ML features, users can continue using the app without internet service. Things like face recognition can still be done quickly without a wifi connection. ML Kit is adding new features all the time, including smart reply and translation, image labeling , facial feature detection, etc. Cloud Functions for Firebase is also out of beta. It includes new features like a crash-litics trigger that can notify you if your site or app crashes and scheduled functions. An emulator is new as well, so you can test without touching your live code. Jen Person Jen is a Developer Advocate at Google. She worked with Firebase for 2.5 years prior to recently joining Google Cloud. She loves building iOS apps with Swift and planning the ideal data structures for various apps using Cloud Firestore. Jen is currently co-starring with JavaScript in a buddy cop comedy where the two don’t see eye to eye but are forced to work together, eventually forming a strong loving bond through a series of hilarious misadventures. Cool things of the week Uploading images directly to Cloud Storage using Signed URL blog Build your own event-sourced system using Cloud Spanner blog Cloud Shell on the Cloud Console app site Google Cloud networking in depth: Cloud Load Balancing deconstructed blog Interview Firebase site Firestore site Cloud Storage site Firebase Authentication site ML Kit site TensorFlow Lite site Cloud Functions for Firebase site Cloud Functions Samples site I/O 2019 Talk: Zero to App video Guide - Cloud Firestore collection group queries docs Guide - Scheduled Cloud Functions docs YouTube - #AskFirebase Playlist videos Codelab - Recognize text, facial features, and objects in images with ML Kit for Firebase: iOS site Codelab - Train and deploy on-device image classification model with AutoML Vision in ML Kit site Codelab - Recognize text, facial features, and objects in images with ML Kit for Firebase: Android site Codelab - Identify objects in images using custom machine learning models with ML Kit for Firebase site Codelab - Detect objects in images with ML Kit for Firebase: Android site Previous episodes on Firebase: GCP Podcast Episode 13: Firebase with Sara Robinson and Vikrum Nijjar podcast GCP Podcast Episode 29: The New Firebase with Abe Haskins and Doug Stevenson podcast GCP Podcast Episode 78: Firebase at I/O 2017 with James Tamplin and Andrew Lee podcast GCP Podcast Episode 97: Cloud Firestore with Dan McGrath and Alex Dufetel podcast GCP Podcast Episode 99: Cloud Functions and Firebase Hosting with David East podcast Question of the week How do I save money on my GCP resources? Where can you find us next? Mark Man will be at Tokyo Next! Watch him live code on Twitch. Mark Mirch is going on vacation!

Firebaseを中心にMeetupや最近のアップデートやGoogle I/O 2019について話しました。 Starring nori 村本章憲(@1amageek) コキチーズ(@k2wanko) Show Notes Firebase Meetup #12 @Google Post Coffee Stripe PORT Firebase x Stripe https://github.com/firebase/functions-samples/tree/Node-8/stripe Firestore FieldValue.increment Cloud Firestoreに追加されたFieldValue.increment()は期待以上！！ Cloud Identity for Customers and Partners https://github.com/firebase/functions-samples/tree/Node-8/line-auth ML Kit language identification: supported languages Google I/O 2019 firebase-cpp-sdk Gatsby.JS dev.to Making dev.to Incredibly fast Hummingbird: Building Flutter for the Web Flutter Create Firebase Dart License FJUG CAST is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. Music Title: Happy and Joyful Children youtu.be/lv1YPeP1O8k Released by: Free Music www.youtube.com/channel/UCqpifMUDQ-HQNO12t7wKBmw

We’re learning all about Cloud SQL this week with our guest, Amy Krishnamohan. Amy’s main job is to teach customers about the products she represents. Today, she explains to Mark and Gabi that Cloud SQL manages services for open source databases, and she spends a little time elaborating on the other database management services Google has to offer. Cloud SQL is a relational data storage solution. Relational data storage is very structured, almost like a table or spreadsheet, making it easier to analyze the data. Cloud SQL is capable of scaling out and up, meaning it can scale for traffic patterns and for storage. In comparison, NoSQL databases are very unstructured. If you’re not sure what kind of data is coming in, you can sort the data first and analyze it later. Each approach has its pros and cons and each is suitable for different types of projects. Recently, Cloud SQL released a feature making it easy to move from on-prem to the cloud. In the future, they will continue to streamline the process of moving between the two spaces. Amy Krishnamohan Amy is Product Marketing Manager at Google Cloud responsible for Databases. She has diverse experience across product marketing, marketing strategy and product management from leading enterprise software companies such as MariaDB, Teradata, SAP, Accenture, Cisco and Intuit. Amy received her Masters in Software Management from Carnegie Mellon University. Cool things of the week Process Workflows with the new Google Docs API blog Jib 1.0.0 is GA—building Java Docker images has never been easier blog GCP Podcast Episode 151: Java & Jib with Patrick Flynn and Mike Eltsufin podcast A guided tour in Google Earth that explores Black history blog Author: Gabe Weiss - Publishing series: Cloud IoT step-by-step Cloud IoT step-by-step: Connecting Raspberry PI + Python site Cloud IoT step-by-step: Cloud to device communication site Cloud IoT step-by-step: Quality of life tip - The command line site Interview Cloud SQL site Cloud SQL Features site MySQL site PostgreSQLsite Cloud MemoryStore site Cloud Bigtable site Cloud Firestore site Cloud Spanner site GCP Podcast Episode 62: Cloud Spanner with Deepti Srivastava podcast Mongo site Getting to know Google Cloud SQL video Question of the week What is a virtual column in a database? Generated columns blog and docs Where can you find us next? Amy will be at the Postgres Conference in New York on March 19. Gabi will be at PHP UK in London and Cloud NEXT in April. Mark will be at GDC in March, Cloud NEXT, and ECG in April. Diamond Partner Q&A: Google’s Mark Mandel Has The Tools To Help You Make Great Games article

Cloud FirestoreがGAになったので、そのアップデートを中心に話しました。 Starring Daiki Matsudate(@d_date) コキチーズ(@k2wanko) Show Notes Firebase Meetup #10 @mercari Cloud Firestore has Gone GA, Lower Pricing Tiers, New Locations, and more! Public Betaは2017年10月 Introducing Cloud Firestore: Our New Document Database for Apps Choose a database: Cloud Firestore or Realtime Database Cloud Firestore Pricing netlify not work hosting.stop() function #1134 License FJUG CAST is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. Music Title: Happy and Joyful Children youtu.be/lv1YPeP1O8k Released by: Free Music www.youtube.com/channel/UCqpifMUDQ-HQNO12t7wKBmw

Firebase Summit 2018でのアップデートを中心に話しました。 Starring Daiki Matsudate(@d_date) コキチーズ(@k2wanko) Show Notes Firebase Japan User Group FJUG Osaka Google Developer Expert Firebase Summit App Dojo Firebaseの概要とFirebase Summit 2018のポイント ML Kit for Firebase Cloud Functions Google Cloud FunctionsがGo言語のサポートを発表 Cloud Firestore Asia Region Local Emulator firebase/functions-samples Using Cloud Functions to update Remote Config in near real-time Google アナリティクス開発者サービス SDK の終了 Time to Upgrade from GCM to FCM Google Analytics for Firebase Demo Project BigQuery Sandbox FJUG Join License FJUG CAST is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. Music Title: Happy and Joyful Children youtu.be/lv1YPeP1O8k Released by: Free Music www.youtube.com/channel/UCqpifMUDQ-HQNO12t7wKBmw

In our last (but not least!) interview from NEXT, Mark and Melanie talked with Sivan Aldor-Noiman and Erik Andrejko about Wellio, an awesome new platform that combines AI and healthy eating. Wellio was developed as a way to not only educate users on the importance of proper nutrition for well-being but to give them their own personal nutritionist. The data scientists at Wellio started from scratch (pun intended) to create their own food-related database and then began training models so the data could be organized and personalized. Using a combination of human power and machine learning techniques, Wellio learns your preferences, allergies, diets, etc. and will make healthy decisions for you based on these key facts. It chooses recipes, populates a grocery list, and even has the ingredients delivered to your door in time for dinner! Sivan Aldor-Noiman Sivan heads Data Science for Wellio, an early stage startup in the FoodTech space that is helping people eat better. In Wellio, her team delivers models that help inspire, empower and adapt to people’s eating needs, cooking abilities and health constraints. She began her career in the Israeli military serving as an instructor for an anti-tank missile unit (please don’t think Rambo, think more like a classroom teacher). Sivan then transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion, Israel Institute of Technology. She moved to the U.S. to complete a Ph.D. degree in Statistics from The Wharton School, University of Pennsylvania. In her previous job, Sivan ended up leading several Data Science teams and learned that she really liked leading technical people since she got to learn a lot from them. Ultimately, she missed the smaller company mentality, so she is back in the startup world. Sivan was once asked to define herself so here goes: “I am an enthusiastic disagreeable giver and a constant empirical driven learner”. Erik Andrejko Erik has spent his career making a positive impact on the world through mathematics. He is a co-founder and Chief Technology Officer of Wellio - an early stage startup applying AI to the intersection of food and human health. Previously, Erik lead the data science and research organization at The Climate Corporation, which applies data science to solve challenging problems in numerous domains including climatology, agronomic modeling and geospatial applications. When not analyzing interesting datasets, Erik can often be found riding up some incline on a bicycle or cooking. Cool things of the week Summary of Google Cloud Next Tokyo site Deep Learning Indaba GCP Credit Awards site Data Studio and Dataprep are now generally available blog DS: BI analyze more than 500 other data sources via more than 100 partner-built connectors and used by over a million people globally DP: new look, team collab and more analytics features blog Announcing general availability of Cloud Memorystore for Redis blog Coursera Advanced Machine Learning with TensorFlow with GCP blog Webinar on October 9th at 9AM PST to learn more Simplifying ML predictions with Google Cloud Functions blog 50 Best Cloud Security Podcasts site GCP Podcast Episode #100: Vint Cerf: past, present, and future of the internet podcast Interview Wellio site GKE site Cloud Storage site Pub/Sub site Cloud Composer site Cloud ML Engine site Stackdriver site Cloud Functions site TensorFlow site Keras site Scikit Learn site Cloud TPU site Cloud AutoML site Cloud Vision site DevOps201 for Application Developers video Cloud Firestore site Day 3 Keynote: Made Here Together video Spinnaker site Contact Wellio email Questions of the week Is Inbox going away? Inbox is signing off: find your favorite features in the new Gmail blog 5 ways the new Gmail can help you get more done blog Where can you find us next? We’ll both be at Strangeloop. Mark will probably be at Unite L.A. in October. Melanie speaking at Monktoberfest Oct 4th in Portland, Maine.

Jeff Dean, the lead of Google AI, is on the podcast this week to talk with Melanie and Mark about AI and machine learning research, his upcoming talk at Deep Learning Indaba and his educational pursuit of parallel processing and computer systems was how his career path got him into AI. We covered topics from his team’s work with TPUs and TensorFlow, the impact computer vision and speech recognition is having on AI advancements and how simulations are being used to help advance science in areas like quantum chemistry. We also discussed his passion for the development of AI talent in the content of Africa and the opening of Google AI Ghana. It’s a full episode where we cover a lot of ground. One piece of advice he left us with, “the way to do interesting things is to partner with people who know things you don’t.” Listen for the end of the podcast where our colleague, Gabe Weiss, helps us answer the question of the week about how to get data from IoT core to display in real time on a web front end. Jeff Dean Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow, leading Google AI and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing. Cool things of the week Google Dataset Search is in beta site Expanding our Public Datasets for geospatial and ML-based analytics blog Zip Code Tabulation Area (ZCTA) site Google AI and Kaggle Inclusive Images Challenge site We are rated in the top 100 technology podcasts on iTunes site What makes TPUs fine-tuned for deep learning? blog Interview Jeff Dean on Google AI profile Deep Learning Indaba site Google AI site Google AI in Ghana blog Google Brain site Google Cloud site DeepMind site Cloud TPU site Google I/O Effective ML with Cloud TPUs video Liquid cooling system article DAWNBench Results site Waymo (Alphabet’s Autonomous Car) site DeepMind AlphaGo site Open AI Dota 2 blog Moustapha Cisse profile Sanjay Ghemawat profile Neural Information Processing Systems Conference site Previous Podcasts GCP Podcast Episode 117: Cloud AI with Dr. Fei-Fei Li podcast GCP Podcast Episode 136: Robotics, Navigation, and Reinforcement Learning with Raia Hadsell podcast TWiML & AI Systems and Software for ML at Scale with Jeff Dean podcast Additional Resources arXiv.org site Chris Olah blog Distill Journal site Google’s Machine Learning Crash Course site Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville book and site NAE Grand Challenges for Engineering site Senior Thesis Parallel Implementations of Neural Network Training: Two Back-Propagation Approaches by Jeff Dean paper and tweet Machine Learning for Systems and Systems for Machine Learning slides Question of the week How do I get data from IoT core to display in real time on a web front end? Building IoT Applications on Google Cloud video MQTT site Cloud Pub/Sub site Cloud Functions site Cloud Firestore site Where can you find us next? Melanie is at Deep Learning Indaba and Mark is at Tokyo NEXT. We’ll both be at Strangeloop end of the month. Gabe will be at Cloud Next London and the IoT World Congress.

mizchiさん、雨宮氏とキーボード、React、Redux、Firebase、WebAssembly、Flutter、Web Componentsなどについて話しました。 ErgoDox users meet up (2016) キーボード二刀流のススメ | Nekoya Press Kinesis Dvorak配列 - Wikipedia なぜ仮想DOMという概念が俺達の魂を震えさせるのか (2014) Redux Refactoring Reducers Example You Might Not Need Redux dailymotion/vast-client-js rails/sprockets airbnb/hypernova reactjs/react-rails 今、SPA/ReactNativeにとっての必要な PaaS を考える Node/SPAエンジニアにとっての富豪的Firebase Hosting Firebase Authentication Authenticate with Firebase Anonymously Using JavaScript Cloud Firestore Amazon Cognito ユーザープールのトークンの使用 AWS AppSync Firebase Functions 上に GraphQL サーバーを実装する Facebook Query Language (FQL) grpc Pattern: API Gateway / Backend for Front-End WebAssembly Optimistic UIs in under 1000 words serde-rs/serde RustをEmscriptenなしでwasmにコンパイルしてNode.jsから呼び出す A Tour of the Flutter Widget Framework Flutter感想 Google Fuchsia Buttons: Floating Action Button HTML Imports skatejs/skatejs Tracking unhandled rejected Promises フロントエンドの負債と向き合う

Vue events around the world, interview with Evan You, how not to Vue, VENoM Stack, debugging in Chrome + VS Code, interactive style guides, and Cloud FireStore w/vue-firestore. See http://news.vuejs.org for links.

Our latest podcast is here. The topic this time is modern app development, innovation at Google and how Cloud Firestore fits into this story. Do you want to take app development to a new level? In this podcast Filip Van Laenen, Cecilie Haugstvedt, Rustam Mehmandarov, Jørn Are Hatlelid and our guest from Google, Dan McGrath, will give a brief overview of Cloud Firestore and Firebase, as well as other highlights and general innovation at Google.

Dan McGrath and Alex Dufetel join Francesc and Mark in the studio this week to discuss Cloud Firestore, the brand new, fully-managed NoSQL document database for mobile and web app development. About Dan McGrath Dan McGrath is the Product Manager for Cloud working on databases such as Cloud Firestore. Dan has spent the last decade working in product & engineering for large scale database systems. He has a background in banking software, databases, and information security. About Alex Dufetel Alex Dufetel is a Product Manager for the Firebase team at Google, working on Backend-as-Service products such as the Realtime Database and Cloud Firestore. Alex was previously Director of Products at Fuze, a video conferencing and enterprise communications provider and, before that, a co-founder of LiveMinutes, a real-time team collaboration app. Cool things of the week Extending per second billing in Google Cloud blog PHP 7.1 for Google App Engine is generally available blog Java 8 on App Engine standard environment is now generally available blog migration Kubernetes 1.8: Security, Workloads and Feature Depth blog Google Container Engine - Kubernetes 1.8 takes advantage of the cloud built for containers blog Announcing Cloud IoT Core public beta blog Interview Cloud Firestore announcement site docs Cloud Firestore server sdks docs Extend Cloud Firestore with Cloud Functions docs Cloud Firestore for Realtime Database Developers blog Firestore Discuss google-group Firestore Realtime Database site docs Question of the week How do I import/export data from my Cloud Datastore? Exporting and Importing Entities docs Scheduling an Export docs Where can you find us next? Francesc just released a new #justforfunc and he'll be presenting at Go Meetup London, Velocity London, Google Cloud Summit Paris and Devfest Nantes He is heading to Australia for GDG Devfest Melbourne and Game Connect Asia Pacific and will be hanging out at Unite Melbourne and PAX Australia.